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Im2Col.cpp
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Im2Col.cpp
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/core/Tensor.h>
#include <ATen/Dispatch.h>
#include <ATen/TensorUtils.h>
#include <ATen/native/im2col.h>
#include <ATen/native/im2col_shape_check.h>
#include <c10/util/irange.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#else
#include <ATen/ops/col2im_native.h>
#include <ATen/ops/empty_like.h>
#include <ATen/ops/im2col_native.h>
#endif
namespace at::native {
namespace {
static void im2col_out_cpu_template(
Tensor& output,
const Tensor& input_,
IntArrayRef kernel_size,
IntArrayRef dilation,
IntArrayRef padding,
IntArrayRef stride) {
TORCH_CHECK(
kernel_size.size() == 2,
"It is expected kernel_size equals to 2, but got size ",
kernel_size.size());
TORCH_CHECK(
dilation.size() == 2,
"It is expected dilation equals to 2, but got size ",
dilation.size());
TORCH_CHECK(
padding.size() == 2,
"It is expected padding equals to 2, but got size ",
padding.size());
TORCH_CHECK(
stride.size() == 2,
"It is expected stride equals to 2, but got size ",
stride.size());
int64_t kernel_height = kernel_size[0];
int64_t kernel_width = kernel_size[1];
int64_t dilation_height = dilation[0];
int64_t dilation_width = dilation[1];
int64_t pad_height = padding[0];
int64_t pad_width = padding[1];
int64_t stride_height = stride[0];
int64_t stride_width = stride[1];
im2col_shape_check(
input_,
Tensor(),
kernel_height,
kernel_width,
dilation_height,
dilation_width,
pad_height,
pad_width,
stride_height,
stride_width);
Tensor input = input_.contiguous();
bool batched_input = true;
if (input.dim() == 3) {
batched_input = false;
input = input.view({1, input.size(0), input.size(1), input.size(2)});
}
int64_t batch_size = input.size(0);
int64_t n_input_plane = input.size(1);
int64_t input_height = input.size(2);
int64_t input_width = input.size(3);
int64_t output_height = (input_height + 2 * pad_height -
(dilation_height * (kernel_height - 1) + 1)) /
stride_height +
1;
int64_t output_width = (input_width + 2 * pad_width -
(dilation_width * (kernel_width - 1) + 1)) /
stride_width +
1;
int64_t n_output_plane = n_input_plane * kernel_width * kernel_height;
int64_t output_length = output_height * output_width;
output.resize_({batch_size, n_output_plane, output_length});
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND3(kBFloat16, kHalf, kBool,
input.scalar_type(), "im2col_out_cpu", [&] {
Tensor input_n;
Tensor output_n;
for (const auto elt : c10::irange(batch_size)) {
input_n = input.select(0, elt);
output_n = output.select(0, elt);
im2col<scalar_t>(
input_n.const_data_ptr<scalar_t>(),
n_input_plane,
input_height,
input_width,
output_height,
output_width,
kernel_height,
kernel_width,
pad_height,
pad_width,
stride_height,
stride_width,
dilation_height,
dilation_width,
output_n.mutable_data_ptr<scalar_t>());
}
if (!batched_input) {
output.resize_({n_output_plane, output_length});
}
});
}
} // namespace
Tensor& im2col_out_cpu(const Tensor& input,
IntArrayRef kernel_size,
IntArrayRef dilation,
IntArrayRef padding,
IntArrayRef stride,
Tensor& output) {
im2col_out_cpu_template(
output, input, kernel_size, dilation, padding, stride);
return output;
}
Tensor im2col_cpu(
const Tensor& input,
IntArrayRef kernel_size,
IntArrayRef dilation,
IntArrayRef padding,
IntArrayRef stride) {
Tensor output = at::empty_like(input, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
im2col_out_cpu_template(
output, input, kernel_size, dilation, padding, stride);
return output;
}
} // namespace at::native